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90884df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """Focused production seams for learned local continuation."""
from __future__ import annotations
from pathlib import Path
import torch
from torch import nn
from music3lab.editing.learned_audio_continuation import (
Rank4QKVContinuationAdapter,
apply_generated_residual,
continuation_residual_target,
flow_interpolate,
analyze_tail,
composed_seam_metrics,
deterministic_pair_offset,
shared_condition_variant_inputs,
)
from music3lab.editing.learned_audio_continuation_data import (
load_learned_continuation_config,
load_pair_records,
)
from music3lab.editing.learned_audio_continuation_runner import (
cosine_learning_rate,
)
ROOT = Path(__file__).resolve().parents[1]
CORPUS = Path(
"/home/ubuntu/minimax-laion-corpus/versions/"
"interim_tranche_678_due_systemic_bot_auth"
)
class _Attention(nn.Module):
def __init__(self, hidden: int) -> None:
super().__init__()
self.to_q = nn.Linear(hidden, hidden, bias=False)
self.to_k = nn.Linear(hidden, hidden, bias=False)
self.to_v = nn.Linear(hidden, hidden, bias=False)
class _Block(nn.Module):
def __init__(self, hidden: int) -> None:
super().__init__()
self.attn = _Attention(hidden)
class _Flow(nn.Module):
def __init__(self, layers: int, hidden: int) -> None:
super().__init__()
self.transformer_blocks = nn.ModuleList(
[_Block(hidden) for _ in range(layers)]
)
def forward(
self,
*,
hidden_states: torch.Tensor,
timestep: torch.Tensor,
encoder_hidden_states: torch.Tensor,
return_dict: bool,
) -> tuple[torch.Tensor]:
del timestep, return_dict
probe = encoder_hidden_states[:, :, :8]
for block in self.transformer_blocks:
probe = (
block.attn.to_q(probe)
+ block.attn.to_k(probe)
+ block.attn.to_v(probe)
) / 3
update = probe.mean(dim=(1, 2)).view(-1, 1, 1)
return (hidden_states + update,)
def test_exact_config_and_frozen_542_68_68_source_inventory() -> None:
loaded = load_learned_continuation_config(
ROOT / "configs" / "learned-audio-continuation-v1.yaml"
)
records = load_pair_records(CORPUS, loaded)
assert {key: len(value) for key, value in records.items()} == {
"train": 542,
"validation": 68,
"heldout": 68,
}
assert loaded.config.projector.target_visible_to_conditioner is False
assert loaded.config.projector.champion_eligible is False
assert loaded.config.claims.handcrafted_baseline_reclassified is False
assert loaded.config.training.batch_size == 16
assert (
loaded.config.target_parameterization
== "target_minus_repeat_tail"
)
assert loaded.config.generated_latent_parameterization == (
"repeat_tail_plus_generated_residual"
)
def test_residual_coordinate_reconstructs_next_and_shares_anchor_noise() -> None:
anchor = torch.tensor(
[[[1.0, 2.0], [3.0, 4.0]]],
dtype=torch.float32,
)
target = torch.tensor(
[[[5.0, 7.0], [11.0, 13.0]]],
dtype=torch.float32,
)
noise = torch.tensor(
[[[0.5, -0.5], [1.5, -1.5]]],
dtype=torch.float32,
)
residual = continuation_residual_target(target, anchor)
terminal = flow_interpolate(noise, residual, torch.ones(1))
assert torch.equal(terminal, residual)
assert torch.equal(apply_generated_residual(anchor, terminal), target)
assert torch.equal(
apply_generated_residual(anchor, torch.zeros_like(anchor)),
anchor,
)
conditions = {
"conditional": torch.ones(1, 2, 3),
"zero_context": torch.zeros(1, 2, 3),
"unrelated_context": -torch.ones(1, 2, 3),
}
variants = shared_condition_variant_inputs(
context_anchor=anchor,
noise_latent=noise,
conditions=conditions,
)
assert set(variants) == set(conditions)
assert all(
shared_anchor is anchor and shared_noise is noise
for shared_anchor, shared_noise, _ in variants.values()
)
assert all(
torch.equal(
apply_generated_residual(shared_anchor, torch.zeros_like(shared_anchor)),
anchor,
)
for shared_anchor, _, _ in variants.values()
)
def test_rank_qkv_hooks_are_only_trainables_and_context_changes_same_noise() -> None:
flow = _Flow(2, 8)
adapter = Rank4QKVContinuationAdapter(
flow, layers=2, hidden_size=8, rank=2
)
try:
assert adapter.trainable_parameter_count() == 2 * 3 * 2 * 8 * 2
assert all(not value.requires_grad for value in flow.parameters())
noise = torch.zeros(1, 128, 86)
time = torch.zeros(1)
zero = torch.zeros(1, 86, 2048)
context = torch.ones_like(zero)
left = adapter.predict_cfg_velocity(
noise, time, zero, guidance_scale=1.7
)
with torch.no_grad():
adapter.q_up[0].fill_(0.1)
right = adapter.predict_cfg_velocity(
noise, time, context, guidance_scale=1.7
)
assert not torch.equal(left, right)
finally:
adapter.close()
def test_exact_zero_trim_retains_near_zero_and_composed_metrics_are_finite() -> None:
source = torch.ones(1, 2, 5000) * 1e-12
source[:, :, -4:] = 0
assert analyze_tail(source).shape[-1] == 4996
append = torch.ones(1, 2, 44032) * 0.01
metric = composed_seam_metrics(
source[:, :, :4996],
append,
overlap_samples=1024,
derivative_absolute_floor=1e-5,
rms_absolute_floor=1e-4,
)
assert metric.boundary_derivative_ratio >= 0
assert metric.overlap_rms_log_error >= 0
def test_crop_and_schedule_endpoints_are_deterministic() -> None:
digest = "1" * 64
first = deterministic_pair_offset(
digest, frame_count=1_000_000, guard_samples=220_500
)
second = deterministic_pair_offset(
digest, frame_count=1_000_000, guard_samples=220_500
)
assert first == second
assert cosine_learning_rate(0, 1200, 5e-5, 5e-6) == 5e-5
assert abs(
cosine_learning_rate(1199, 1200, 5e-5, 5e-6) - 5e-6
) < 1e-12
|